Battery negative potential evaluation system, method, and electric vehicle

Abstract

The application relates to the technical field of batteries, and provides a battery negative electrode potential evaluation system, a battery negative electrode potential evaluation method and an electric vehicle. The system comprises a data acquisition device configured to acquire battery surface stress data; and a controller configured to: determine a current high stress concentration area and an area of the current high stress concentration area based on current battery surface stress data; determine a current change rate of the area of the high stress concentration area with respect to a battery SOC based on the area of the current high stress concentration area, a current battery SOC, a historical area of the high stress concentration area and a historical battery SOC; and in the case that the current change rate is greater than a preset change rate threshold, substitute the current battery SOC into a mapping relationship model of the SOC and the negative electrode potential to obtain a current battery negative electrode potential. The application does not need to depend on a complex electrochemical model or additional hardware, has low calculation cost, good real-time performance, can be deployed on a large scale at a vehicle end, and is beneficial to early warning of lithium precipitation risk before lithium precipitation occurs.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to battery negative electrode potential evaluation systems, methods, and electric vehicles. Background Technology

[0002] With the development of technology, lithium-ion batteries have become a key power source for electronic devices, electric vehicles, and energy storage systems. During the charging and discharging process of lithium-ion batteries, it is necessary to deduce the negative electrode potential of the battery in order to detect the risk of lithium plating.

[0003] However, traditional battery negative electrode potential monitoring methods typically require a reference electrode or a complex state observer to accurately measure the negative electrode potential value, which places high demands on hardware and computing power. Therefore, existing battery negative electrode potential monitoring methods rely on complex electrochemical models or additional hardware, resulting in high computational costs, poor real-time performance, and difficulty in large-scale deployment in vehicles, thus leading to untimely detection of lithium plating risk. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in related technologies. To this end, this application proposes a battery negative electrode potential evaluation system that does not rely on complex electrochemical models or additional hardware. It not only has low computational cost and good real-time performance, enabling large-scale deployment in vehicles, but also helps to identify whether lithium plating is about to occur before it happens, thus achieving early warning of lithium plating risk.

[0005] This application also proposes a method for evaluating the negative electrode potential of a battery.

[0006] This application also proposes an electric vehicle.

[0007] The battery negative electrode potential evaluation system according to an embodiment of this application includes: Data acquisition equipment is used to collect battery surface stress data; The controller is configured to: Based on the current battery surface stress data, determine the current high stress concentration area and its area; Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC. If the current rate of change is greater than a preset rate of change threshold, the current battery SOC is substituted into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0008] The battery negative electrode potential evaluation method according to the embodiments of this application includes: Based on the current battery surface stress data, determine the current high stress concentration area and its area; Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC. If the current rate of change is greater than a preset rate of change threshold, the current battery SOC is substituted into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0009] An electric vehicle according to an embodiment of this application includes a battery pack and the aforementioned battery negative electrode potential evaluation system, wherein the battery pack includes a plurality of batteries; For each battery, under on-board charging conditions, the negative electrode potential of the battery is evaluated using the battery negative electrode potential evaluation system to obtain the current negative electrode potential of the battery, so as to adjust the current charging strategy corresponding to the battery according to the current negative electrode potential of the battery.

[0010] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: This application's embodiments determine the current high-stress concentration region and its area based on current battery surface stress data. Based on the current high-stress concentration region area, current battery SOC, historical high-stress concentration region areas, and historical battery SOC, the current rate of change of the high-stress concentration region area with respect to battery SOC is determined. If the current rate of change exceeds a preset threshold, the current battery SOC is substituted into the SOC-negative electrode potential mapping model, achieving timely and accurate assessment of the current battery negative electrode potential. This application establishes a correlation between the high-stress concentration region area and the battery negative electrode potential by linking the rate of change of the high-stress concentration region area with respect to battery SOC to the battery phase transition stage, and by establishing the SOC-negative electrode potential mapping relationship. Based on this correlation, the battery negative electrode potential can be monitored and assessed without relying on complex electrochemical models or additional hardware. This not only has low computational cost and good real-time performance, enabling large-scale deployment in vehicles, but also facilitates early identification of impending lithium plating before it occurs, providing early warning of lithium plating risks.

[0011] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application and are not considered as limitations on this application. Moreover, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the battery negative electrode potential evaluation system provided in the embodiments of this application.

[0014] Figure 2 This is a schematic diagram of the stress distribution of each sensing unit provided in the embodiments of this application.

[0015] Figure 3 This is a schematic diagram of the curve showing the change of the area of ​​the high stress concentration region with respect to the battery SOC, provided in an embodiment of this application.

[0016] Figure 4 This is a schematic diagram of the mapping relationship model between SOC and negative electrode potential provided in the embodiments of this application.

[0017] Figure 5 This is a schematic flowchart of the battery negative electrode potential evaluation method provided in the embodiments of this application.

[0018] Figure 6 This is a schematic diagram of the structure of an electric vehicle provided in an embodiment of this application.

[0019] Figure 7 This is a schematic diagram of the battery negative electrode potential evaluation device provided in the embodiments of this application.

[0020] Figure 8 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0022] The following is combined with Figures 1 to 8 This application describes a battery negative electrode potential evaluation system, method, and electric vehicle.

[0023] Currently, judging from market trends, the application of power batteries is becoming increasingly widespread. Power batteries are not only used in energy storage systems such as hydropower, thermal power, wind power, and solar power plants, but also extensively used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. With the continuous expansion of power battery applications, market demand is also constantly increasing.

[0024] Figure 1 This is a schematic diagram of the battery negative electrode potential evaluation system provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a battery negative electrode potential evaluation system, including: Data acquisition device 110 is used to collect battery surface stress data; Controller 120, the controller being configured to: Based on the current battery surface stress data, determine the current high stress concentration area and its area; Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC. If the current rate of change is greater than a preset rate of change threshold, the current battery SOC is substituted into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0025] The embodiments of this application are applicable to the evaluation of the negative electrode potential of various types of lithium-ion batteries. For example, ternary (NCM) batteries, lithium iron phosphate (LFP) batteries, lithium nickel manganese oxide batteries, lithium cobalt oxide batteries, etc.

[0026] Data acquisition equipment can refer to a sensor capable of sensing the stress distribution on the battery surface and outputting corresponding electrical signals. In the embodiments of this application, the data acquisition equipment can employ a thin-film pressure sensor. By deploying a thin-film pressure sensor on the battery surface, the mechanical stress generated on the battery surface due to the multiphase transformation of the electrode material during the charging and discharging process of the lithium-ion battery can be captured. This allows for the measurement of the stress distribution at different locations on the battery surface, thereby obtaining battery surface stress data.

[0027] In some embodiments, the thin-film pressure sensor may be a flexible thin-film pressure sensor, which has high flexibility and good surface adhesion, thereby enabling accurate measurement of stress distribution at different locations on the battery surface, which is beneficial to improving the accuracy of battery negative electrode potential assessment.

[0028] In some embodiments, during battery charging, battery surface stress data can be continuously collected at a preset sampling frequency f, while the current battery SOC (State of Charge) and charging current are recorded.

[0029] During the charging and discharging process of lithium-ion batteries, the electrode materials undergo multiphase transitions (phase transitions). During these phase transitions, the lattice constant and volume of the electrode materials change, leading to a reconstruction of the stress distribution on the battery surface. Related studies have used overall expansion force or average stress to monitor battery status, but have not focused on the evolution of high-stress concentration regions and its correlation with phase transitions.

[0030] High stress concentration regions can refer to areas where local stress is significantly higher than the average stress. In the embodiments of this application, experiments have shown that the area of ​​high stress concentration regions on the battery surface exhibits a step-like evolution curve corresponding to the phase transition stage as the battery's SOC changes. At the end of charging (i.e., the last phase transition stage of the battery), the area of ​​high stress concentration regions increases rapidly, and the risk of lithium plating also increases significantly at this time.

[0031] Therefore, this embodiment of the application determines the current high-stress concentration region and its area based on real-time collected current battery surface stress data during battery charging and discharging. It then determines the current rate of change of the high-stress concentration region area with respect to the battery's SOC, thus linking this rate of change to the battery's phase transition stage. By comparing the current rate of change with a preset threshold, it can promptly and accurately determine whether the battery has entered the final phase transition stage. This allows for timely assessment of the battery's negative electrode potential even when the final phase transition stage has been reached, facilitating early warning of lithium plating risk. Compared to methods based on stress distribution standard deviation, the high-stress concentration region in this application is more sensitive to local extreme expansion and has a higher signal-to-noise ratio. While the stress distribution standard deviation reflects the overall dispersion, the high-stress concentration region in this application directly captures areas requiring focused attention. These areas are often where lithium plating is most likely to occur first, making the lithium plating risk warning more direct and reliable.

[0032] In another embodiment, the proportion of high-stress concentration areas on the battery surface can also exhibit a step-like evolution curve corresponding to the phase transition stage as the battery's state of charge (SOC) changes. Based on the current area of ​​the high-stress concentration area, the ratio of the current high-stress concentration area to the battery surface area can be calculated to obtain the current proportion of the high-stress concentration area. Based on the current proportion of the high-stress concentration area, the current battery SOC, the historical proportion of the high-stress concentration area, and the historical battery SOC, the current rate of change of the high-stress concentration area with respect to the battery SOC can be determined. By comparing this rate of change with the corresponding rate of change threshold, it can be promptly determined whether the battery has entered the final phase transition stage.

[0033] The current area of ​​high stress concentration region can refer to the area of ​​high stress concentration region calculated at the current moment, which can reflect the severity of local expansion of the battery at the current moment.

[0034] The current battery SOC can refer to the battery SOC collected at the current moment, which is used to characterize the ratio of the current remaining capacity to the rated capacity.

[0035] The area of ​​historical high stress concentration regions can refer to the area of ​​high stress concentration regions at the previous moment, or it can refer to the data sequence of high stress concentration region areas at several moments before the current moment, used to characterize the evolution trend of high stress concentration region areas.

[0036] Historical battery SOC can refer to the battery SOC at the previous moment, or it can refer to the battery SOC data sequence at several moments before the current moment. Historical battery SOC can correspond to the area of ​​historical high stress concentration regions to establish a mapping relationship.

[0037] In this embodiment, the current rate of change of the high-stress concentration area with respect to the battery SOC can be determined based on the current area of ​​the high-stress concentration area, the current battery SOC, the historical area of ​​the high-stress concentration area, and the historical battery SOC. This current rate of change is then compared to a preset rate of change threshold. If the current rate of change is greater than the preset threshold, it indicates that the battery has entered the final phase transition stage, significantly increasing the risk of lithium plating. If the current rate of change is less than or equal to the preset threshold, it indicates that the battery has not yet entered the final phase transition stage. Compared to existing lithium plating detection methods, which often only detect lithium plating after it has already occurred, this application can detect whether the battery has entered the final phase transition stage before lithium plating occurs, facilitating early prediction of lithium plating risk.

[0038] In this embodiment, when the current rate of change is greater than a preset rate of change threshold, i.e., when the current moment is detected as the last phase transition stage of the battery, the current battery negative electrode potential is obtained by substituting the current battery SOC into the mapping relationship model between SOC and negative electrode potential. This does not require a complex electrochemical model or additional hardware, and thus it is possible to identify whether lithium plating is about to occur based on the value of the current battery negative electrode potential. This is beneficial for timely warning of lithium plating risk without relying on a reference electrode.

[0039] The mapping model between SOC and negative electrode potential is a model obtained in advance through extensive offline calibration via numerous experiments, used to characterize the mapping relationship between SOC and negative electrode potential. In this application, the current battery SOC is input into the SOC-negative electrode potential mapping model to evaluate the current battery negative electrode potential. The evaluation process does not rely on complex electrochemical models or additional hardware, has low computational cost, and can be deployed on a large scale in vehicles.

[0040] It should be noted that, in this embodiment, offline calibration can be performed in advance. Based on the data collected during the charging experiment, a curve showing the relationship between the area of ​​the high stress concentration region and the battery's SOC is plotted. This curve exhibits a step-like evolution pattern corresponding to the phase transition stages. Therefore, the last stage in this curve can be identified as the last phase transition stage of the battery. In the last stage, the battery is in the last phase transition stage, and the stress distribution disorder on the battery surface changes from decreasing to rapidly increasing. There is a positive correlation between stress distribution disorder and the area of ​​the high stress concentration region. This application uses the area of ​​the high stress concentration region to accurately quantify and characterize the stress distribution disorder, thereby identifying the inflection point of the last stage in the curve showing the relationship between the area of ​​the high stress concentration region and the battery's SOC as the inflection point where the stress distribution disorder changes from decreasing to rapidly increasing. A preset rate of change threshold is determined based on the rate of change at this inflection point. After determining the inflection point where the stress distribution disorder changes from decreasing to rapidly increasing, a mapping model between SOC and negative electrode potential can be established based on the battery's SOC at the inflection point and the corresponding negative electrode potential.

[0041] In some embodiments, an inflection point detection algorithm can be used to detect the inflection point position in the curve of the area of ​​the high stress concentration region with respect to the battery SOC, thereby establishing a mapping relationship model between SOC and negative electrode potential based on the battery SOC at the inflection point and the corresponding battery negative electrode potential.

[0042] Therefore, this application embodiment, through offline experimental calibration, reveals the relationship curve between the area of ​​the high stress concentration region and the battery's SOC, as well as the mapping relationship between SOC and the negative electrode potential, thereby establishing a correlation between the area of ​​the high stress concentration region and the battery's negative electrode potential. During battery charging, this correlation between the area of ​​the high stress concentration region and the battery's negative electrode potential can be used to timely and accurately assess the current battery's negative electrode potential based on the current area of ​​the high stress concentration region, thus making lithium plating risk warnings more reliable and timely.

[0043] This application's embodiments determine the current high-stress concentration region and its area based on current battery surface stress data. Based on the current high-stress concentration region area, current battery SOC, historical high-stress concentration region areas, and historical battery SOC, the current rate of change of the high-stress concentration region area with respect to battery SOC is determined. If the current rate of change exceeds a preset threshold, the current battery SOC is substituted into the SOC-negative electrode potential mapping model, achieving timely and accurate assessment of the current battery negative electrode potential. This application establishes a correlation between the high-stress concentration region area and the battery negative electrode potential by linking the rate of change of the high-stress concentration region area with respect to battery SOC to the battery phase transition stage, and by establishing the SOC-negative electrode potential mapping relationship. Based on this correlation, the battery negative electrode potential can be monitored and assessed without relying on complex electrochemical models or additional hardware. This not only has low computational cost and good real-time performance, enabling large-scale deployment in vehicles, but also facilitates early identification of impending lithium plating before it occurs, providing early warning of lithium plating risks.

[0044] Based on any of the above embodiments, the data acquisition device is a sensor array deployed on the battery surface, the sensor array including multiple sensing units, and the battery surface stress data being the stress data of each sensing unit; determining the current high stress concentration area based on the current battery surface stress data includes: Based on the current stress data of each sensing unit, the sensing unit whose current stress data meets the criteria for high stress concentration point determination is identified as the current high stress concentration point. Based on the location of each current high stress concentration point, the current high stress concentration area is determined.

[0045] In some embodiments, a thin-film pressure sensor array can be deployed on at least one surface of the battery. The sensor array may include M×N sensing units, which are arranged in a grid pattern on the battery surface at a preset spacing to cover the central and edge regions of the battery. Here, M and N are both positive integers greater than 1.

[0046] In some embodiments, the stress data collected by each sensing unit can be recorded as σ(i,j,t), where (i,j) is the position coordinate of the sensing unit in the sensor array, and t is the current sampling time.

[0047] In some embodiments, it can be determined whether the current stress data collected by each sensing unit meets the criteria for determining a high stress concentration point. If the current stress data collected by any sensing unit meets the criteria for determining a high stress concentration point, then that sensing unit is determined as the current high stress concentration point, and the current high stress concentration area is determined based on the location of each current high stress concentration point.

[0048] Figure 2 This is a schematic diagram of the stress distribution of each sensing unit provided in the embodiments of this application. (Refer to...) Figure 2 Multiple stress zones can be defined, each corresponding to a different color. If the stress data of a sensing unit falls within the stress zone corresponding to a red label, the location of that sensing unit is marked red; if it falls within the stress zone corresponding to a green label, it is marked green; and if it falls within the stress zone corresponding to a blue label, it is marked blue. The redder the color, the greater the stress data of the sensing unit; the bluer the color, the smaller the stress data. This method provides a clear visual representation of the stress values ​​at each sensing unit's location.

[0049] This application embodiment identifies the current high stress concentration point as the sensing unit whose current stress data meets the criteria for high stress concentration point determination. Based on the location of each current high stress concentration point, the current high stress concentration region is determined. This fully explores the physical correlation between stress distribution characteristics and the internal electrochemical state (i.e., phase transition) of the battery, improves the utilization efficiency of stress information, and is conducive to further establishing the correlation between the area of ​​the high stress concentration region and the negative electrode potential of the battery.

[0050] Based on any of the above embodiments, the condition for determining the high stress concentration point is that the current stress data is greater than the current average stress value by a preset multiple; wherein, the preset multiple is greater than 1, and the current average stress value is calculated based on the current stress data of each sensing unit.

[0051] In some embodiments, for each sampling time, the average stress μ of the current stress data of all sensing units can be calculated, and the high stress concentration point determination condition can be defined as: σ(i,j)>k×μ; Where σ(i,j) is the current stress data collected by the sensing unit, k is a preset multiple, and μ is the average stress value μ of the current stress data of all sensing units.

[0052] In some embodiments, the preset multiplier k can be set to 1.5, meaning that sensing units whose current stress data is greater than 1.5 times the average stress value can be considered as high stress concentration points. In practical applications, the preset multiplier k can also be set to other values ​​greater than 1, and this application does not impose specific limitations.

[0053] In another embodiment, the high stress concentration point determination condition can also be defined as the current stress data being greater than a preset stress threshold, that is, a sensing unit whose current stress data is greater than a preset stress threshold can be regarded as a high stress concentration point.

[0054] In another embodiment, the criteria for determining a high stress concentration point may include multiple sub-criteria. A sensing unit whose current stress data simultaneously satisfies multiple sub-criteria can be considered a high stress concentration point. For example, a sensing unit whose current stress data is greater than 1.5 times the average stress value and greater than a preset stress threshold can be considered a high stress concentration point.

[0055] This application embodiment sets the high stress concentration point determination condition to the current stress data being greater than a preset multiple of the current average stress value. This can adaptively and dynamically eliminate the influence of overall stress drift and only identify relatively prominent local stress peaks, which is beneficial to improving the robustness of lithium plating early warning and its adaptability to different working conditions.

[0056] Based on any of the above embodiments, the data acquisition device is a sensor array deployed on the battery surface, the sensor array including multiple sensing units; the current high stress concentration region includes multiple current high stress concentration points; determining the area of ​​the current high stress concentration region includes: Calculate the area of ​​the current high stress concentration region based on the number of current high stress concentration points and the area of ​​a single sensing unit.

[0057] In some embodiments, the area R_HS (High Stress Region) of the high stress concentration region can be calculated using the following formula: R_HS = Number of current high stress concentration points × Area of ​​a single sensor unit; In some embodiments, the number of current high stress concentration points can be determined based on the number of sensing units that satisfy σ(i,j)>1.5μ.

[0058] This application embodiment calculates the area of ​​the current high stress concentration region based on the number of current high stress concentration points and the area of ​​a single sensing unit, fully exploring the physical correlation between stress distribution characteristics and the internal electrochemical state (i.e., phase transition) of the battery, improving the utilization efficiency of stress information, and thus facilitating the further establishment of the correlation between the area of ​​the high stress concentration region and the negative electrode potential of the battery.

[0059] Based on any of the above embodiments, determining the current rate of change of the high stress concentration region area with respect to the battery SOC based on the current high stress concentration region area, the current battery SOC, the historical high stress concentration region area, and the historical battery SOC includes: Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, a curve showing the current relationship between the area of ​​the high stress concentration region and the battery SOC is fitted. Calculate the derivative of the current relationship curve at the current battery SOC to obtain the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC.

[0060] During battery charging, as lithium ions continuously embed into the negative electrode active material, the negative electrode volume expands, and the stress distribution on the battery surface gradually changes. When there is a risk of lithium plating in a local area, lithium dendrite growth will affect the stress data in that area, forming high stress concentration points and expanding the area of ​​these high stress concentration points.

[0061] This application embodiment collects current battery SOC data in real time and calculates the current area of ​​high stress concentration region in real time. Based on current data and historical data, it can fit the current relationship curve between the area of ​​high stress concentration region and battery SOC in real time. Based on the real-time fitted relationship curve, it determines the current rate of change of the area of ​​high stress concentration region with respect to battery SOC. Thus, it links the rate of change of the area of ​​high stress concentration region with respect to battery SOC to the battery phase transition stage. By comparing the current rate of change with a preset rate of change threshold, it can timely and accurately determine whether the battery has entered the last phase transition stage, which is conducive to achieving early warning of lithium plating risk.

[0062] In some embodiments, data functions such as polynomials, spline curves, or local weighted regression can be used to fit the curve of the relationship between the area of ​​the continuously differentiable high stress concentration region and the current state of battery SOC.

[0063] In this embodiment, the first derivative of the curve relating the area of ​​the high stress concentration region to the current battery SOC can be calculated at the current battery SOC to obtain the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC.

[0064] In another embodiment, the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC can be obtained based on the current area of ​​the high stress concentration region, the current battery SOC, the previous area of ​​the high stress concentration region, and the previous battery SOC.

[0065] Based on any of the above embodiments, the controller is further configured to: Based on the comparison between the current negative electrode potential of the battery and the preset safety potential threshold, the current charging strategy corresponding to the battery is determined.

[0066] A preset safety potential threshold can be used to characterize the safety potential to prevent lithium plating. In some embodiments, the preset safety potential threshold can be set to 0V or 20mV, and this application does not impose any limitations on this.

[0067] In this embodiment, by comparing the current negative electrode potential of the battery with a preset safe potential threshold, the system can identify in advance whether lithium plating is about to occur based on the comparison result, thereby achieving early warning of lithium plating risk. This allows the system to adjust the current charging strategy of the battery based on the comparison result to avoid lithium plating.

[0068] Based on any of the above embodiments, determining the current charging strategy corresponding to the battery based on the comparison result of the current battery negative electrode potential and the preset safety potential threshold includes: If the current negative electrode potential of the battery is greater than the preset lithium plating critical potential, the current charging strategy corresponding to the battery is determined to be the continue charging strategy. If the current negative electrode potential of the battery is less than or equal to the preset lithium plating critical potential, the current charging strategy corresponding to the battery is determined to be a protective charging strategy.

[0069] In the embodiments of this application, the charging strategy corresponding to the battery may include a continuous charging strategy and a protective charging strategy.

[0070] The "continue charging strategy" can refer to continuing or maintaining charging. If the current negative electrode potential of the battery is greater than the preset lithium plating threshold potential, it indicates that lithium plating has occurred or is in progress. In this case, the charging current can be reduced or charging can be paused.

[0071] Protective charging strategies can refer to reducing the charging current or pausing charging. If the current negative electrode potential of the battery is less than or equal to the preset lithium plating critical potential, it indicates that although there is a risk of lithium plating, lithium plating has not yet occurred, and charging can continue or be maintained.

[0072] This application embodiment can dynamically adjust the current charging strategy of the battery by comparing the current negative electrode potential of the battery with the preset safe potential threshold in real time, thereby maximizing the battery charging efficiency while avoiding lithium plating.

[0073] Based on any of the above embodiments, the mapping relationship model between SOC and negative electrode potential is calibrated in the following way: Based on the continuously collected battery surface stress data and battery SOC under different operating conditions, the variation curves of the area of ​​high stress concentration region with battery SOC under different operating conditions were determined. For each change curve, the battery SOC corresponding to the point where the rate of change of the change curve is greater than a preset positive value is determined as the battery SOC calibration value. Based on the battery SOC calibration value and the corresponding battery negative electrode potential under different operating conditions, a mapping relationship model between SOC and negative electrode potential is established.

[0074] In this embodiment of the application, offline calibration can be used to determine the variation curve of the area of ​​the high stress concentration region with respect to the battery SOC under different operating conditions. Based on the battery SOC calibration value under different operating conditions and the corresponding battery negative electrode potential, a mapping relationship model between SOC and negative electrode potential is established, thereby establishing the relationship between the variation curve and the battery negative electrode potential.

[0075] In some embodiments, a three-electrode battery (including a reference electrode) of the same type can be fabricated, and constant current charging experiments can be conducted at different charging rates (e.g., 0.05C, 0.1C, 0.33C, 0.5C, 1C, 1.5C) and at different temperatures. Stress data from the thin-film sensor array are collected simultaneously, and the variation curve of the area of ​​the high stress concentration region R_HS (High Stress Region) with respect to the battery SOC (i.e., the R_HS(SOC) curve) is calculated.

[0076] Figure 3 This is a schematic diagram showing the change curve of the area of ​​the high stress concentration region with respect to the battery's state of charge (SOC) provided in an embodiment of this application. (Refer to...) Figure 3 Constant current charging experiments can be conducted at 0.05C, 0.33C, and 1C to obtain the variation curves of the area R_HS of the high stress concentration region with respect to the battery SOC at 0.05C, 0.33C, and 1C.

[0077] Analysis of the R_HS(SOC) curve reveals that it exhibits a segmented characteristic. Taking lithium iron phosphate batteries as an example, the R_HS(SOC) curve typically displays three segments: the first segment (SOC around 0%-18%), where R_HS decreases rapidly; the second segment (SOC around 18%-80%), where R_HS plateaus; and the third segment (SOC around 80%-100%), where R_HS increases rapidly.

[0078] In some embodiments, the inflection point of the last stage of the curve of the area of ​​the high stress concentration region with respect to the battery SOC can be defined as the point at which the first derivative of the R_HS(SOC) curve, d(R_HS) / d(SOC), changes from a flat (close to zero) value to a significantly positive value, that is, the point when the rate of change of the curve is greater than a preset positive rate of change, and the battery SOC corresponding to the inflection point is denoted as the battery SOC calibration value SOC_kink.

[0079] It should be noted that during actual charging, if the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC is greater than the preset rate of change threshold, it can be said that the inflection point of the last stage of the curve has been reached, and the battery has entered the last phase transition stage.

[0080] In some embodiments, the battery SOC calibration value SOC_kink and its corresponding negative electrode potential V_neg (obtained through a three-electrode battery) can be recorded for each operating condition. Experiments show that there is a good linear or monotonic relationship between the battery SOC calibration value SOC_kink and the battery negative electrode potential V_neg. A mapping model between SOC and negative electrode potential can be established: V_neg = f(SOC_kink).

[0081] Figure 4 This is a schematic diagram of the mapping relationship model between SOC and negative electrode potential provided in the embodiments of this application. (Refer to...) Figure 4 Based on the battery SOC calibration value SOC_kink at 0.05C, 0.33C, and 1C and the corresponding battery negative electrode potential V_neg, a mapping relationship model between SOC and negative electrode potential can be established.

[0082] Therefore, the SOC position corresponding to the inflection point of the final stage's rising curve (the point where the curve transitions from flat to rapid) has a quantitative mapping relationship with the current negative electrode potential of the battery. By pre-calibrating and establishing a mapping model between the inflection point SOC and the negative electrode potential, the inflection point can be detected online. Based on the battery SOC at the inflection point, the battery's negative electrode potential can be estimated, and the battery charging strategy can be adjusted when the battery's negative electrode potential approaches the lithium plating critical potential (i.e., the preset lithium plating critical potential).

[0083] This application embodiment obtains a pre-calibrated mapping model between SOC and negative electrode potential. This model can be applied to the battery charging process under different operating conditions and has the ability to adapt to charging rate and temperature. The battery SOC corresponding to the inflection point is different under different charging rates, but the calibrated mapping model between SOC and negative electrode potential can be used in a wide range of operating conditions.

[0084] The embodiments of this application can be applied to different battery systems (such as lithium iron phosphate batteries and ternary batteries) by simply adjusting the inflection point detection logic (detecting the last rising segment) according to the number of phase transition segments, and after calibration, it has a wide range of applications.

[0085] Based on any of the above embodiments, the battery is a lithium iron phosphate battery or a ternary battery; When the battery is a lithium iron phosphate battery, the curve showing the change of the area of ​​the high stress concentration region with respect to the battery SOC presents a three-stage change corresponding to the phase transition stage, and the curve shows an upward trend in the last stage of the three stages. When the battery is a ternary lithium battery, the curve showing the change of the area of ​​the high stress concentration region with respect to the battery's SOC exhibits a four-segment change corresponding to the phase transition stage, with the curve showing an upward trend in the last stage of the four segments.

[0086] The area of ​​high stress concentration regions on the battery surface can exhibit a step-like evolution curve corresponding to the phase transition stage as the battery's state of charge (SOC) changes.

[0087] In this embodiment of the application, taking lithium iron phosphate battery as an example, there are three phase transition stages. The curve of the area of ​​high stress concentration region with respect to battery SOC shows a "three-stage" change. In the last stage of the three stages (i.e. the end of charging), the curve shows a rapid upward trend.

[0088] At this point, the preset rate of change threshold used during the charging process of lithium iron phosphate batteries can be determined based on the rate of change at the inflection point between the second and last stages in the three-stage process.

[0089] In some embodiments, the average rate of change can be calculated based on the rate of change corresponding to the inflection point between the second and last stages in the change curves under all operating conditions, and the average rate of change can be determined as a preset rate of change threshold.

[0090] In another embodiment, the minimum rate of change can be selected as the preset rate of change threshold from the rate of change corresponding to the inflection point between the second and last stages in the change curves under all operating conditions.

[0091] In the embodiments of this application, taking a ternary lithium battery as an example, there are four phase transition stages. The curve of the area of ​​the high stress concentration region with respect to the battery SOC shows a "four-segment" change. In the last stage of the four segments (i.e. the end of charging), the curve also shows a rapid upward trend.

[0092] At this point, the preset rate of change threshold used during the charging process of ternary lithium batteries can be determined based on the rate of change at the inflection point between the third and last stages in the four-stage process. The specific determination method of the preset rate of change threshold used during the charging process of ternary lithium batteries is similar to that used during the charging process of lithium iron phosphate batteries, and will not be elaborated here.

[0093] This application, for the first time, establishes a direct correlation between the area of ​​the high stress concentration region and the evolution of the battery's state of charge (SOC) and the battery's phase transition stages, and utilizes the inflection point of the last phase transition stage as a proxy characteristic quantity for the negative electrode potential. By leveraging the three-stage stress evolution characteristics of lithium iron phosphate batteries and the four-stage stress evolution characteristics of ternary lithium batteries, this application achieves timely and accurate assessment of the battery's negative electrode potential without relying on complex electrochemical models or additional hardware. It features low computational cost, good real-time performance, and can be deployed on a large scale in vehicles.

[0094] Based on any of the above embodiments, the mapping relationship model between SOC and negative electrode potential is stored using any one of linear regression, polynomial fitting, or table lookup method.

[0095] The mapping model between SOC and negative electrode potential can refer to a set of functions or rules that describe the mathematical correspondence between the input variable, battery SOC, and the output variable, battery negative electrode potential.

[0096] In this embodiment, a linear regression method can be used to store the mapping relationship model between SOC and negative electrode potential. During battery charging, the current battery SOC can be substituted into the mapping relationship model between SOC and negative electrode potential for linear calculation to obtain the current battery negative electrode potential. The calculation is simple and has low computational cost.

[0097] In this embodiment, a stored polynomial fitting method can be used to store the mapping relationship model between SOC and negative electrode potential. During battery charging, the current battery SOC can be substituted into the mapping relationship model between SOC and negative electrode potential to perform polynomial fitting calculation to obtain the current battery negative electrode potential. The battery negative electrode potential evaluation value obtained by polynomial fitting calculation is more accurate and reliable.

[0098] In this embodiment, a lookup table method can be used to store the mapping relationship model between SOC and negative electrode potential. The discrete values ​​or intervals of battery SOC and the corresponding negative electrode potential evaluation values ​​are pre-calculated and stored in the lookup table. During battery charging, the current battery negative electrode potential can be quickly obtained based on the current battery SOC through direct indexing, linear interpolation, or nearest neighbor interpolation. The lookup table method does not require real-time calculation of mathematical formulas and is suitable for scenarios where controller resources are limited and the mapping relationship is complex and difficult to express with low-order polynomials.

[0099] In some embodiments, charging tests (0-100% SOC) can be performed in a constant temperature chamber (the test environment temperature can be set to 25℃, 45℃, 10℃, etc.). Simultaneously, the negative electrode potential (the measured battery is a three-electrode battery) and stress distribution data on the battery surface collected by a thin-film pressure sensor are monitored. This allows for the plotting of a curve showing the change in the area of ​​the high stress concentration region with respect to the battery SOC. An inflection point detection algorithm is used to detect the inflection point in the curve, and the corresponding battery SOC at the inflection point is extracted. Finally, the test temperature, the lowest negative electrode potential, and the SOC corresponding to the inflection point can be stored in a lookup table.

[0100] The embodiments of this application pre-calibrate the mapping relationship model between SOC and negative electrode potential by using linear regression, polynomial fitting or table lookup method, so that during the battery charging process, the real-time battery negative electrode potential state can be quickly calculated based on real-time stress data without relying on complex electrochemical models or additional hardware.

[0101] Based on any of the above embodiments, when the current rate of change is greater than a preset rate of change threshold, the controller is further configured to: If the current battery SOC is less than or equal to a preset safe SOC threshold, the current charging strategy corresponding to the battery is adjusted.

[0102] In this embodiment, when the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC is greater than a preset rate of change threshold, the current battery SOC can be directly compared with a preset safe SOC threshold, and the current charging strategy corresponding to the battery can be determined based on the comparison result.

[0103] If the current battery SOC is less than or equal to a preset safe SOC threshold, the current charging strategy can be switched from a continuous charging strategy to a protective charging strategy. If the current battery SOC is greater than the preset safe SOC threshold, the current charging strategy can remain the continuous charging strategy.

[0104] In some embodiments, a preset safe potential threshold can be substituted into the mapping relationship model between SOC and negative electrode potential obtained from experimental calibration to obtain the preset safe SOC threshold.

[0105] This application embodiment, by directly comparing the current battery SOC with a preset safe potential threshold when the current rate of change is greater than a preset rate of change threshold, can further accelerate the calculation efficiency and help ensure the timeliness of lithium plating risk warning.

[0106] The battery negative electrode potential assessment method provided in this application is described below. The battery negative electrode potential assessment method described below can be referred to in correspondence with the battery negative electrode potential assessment system described above.

[0107] Figure 5 This is a schematic flowchart of the battery negative electrode potential evaluation method provided in the embodiments of this application. (Refer to...) Figure 5 This application provides a method for evaluating the negative electrode potential of a battery, which may specifically include the following steps: Step 501: Based on the current battery surface stress data, determine the current high stress concentration area and the area of ​​the current high stress concentration area.

[0108] Step 502: Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC.

[0109] Step 503: If the current rate of change is greater than a preset rate of change threshold, substitute the current battery SOC into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0110] This application's embodiments determine the current high-stress concentration region and its area based on current battery surface stress data. Based on the current high-stress concentration region area, current battery SOC, historical high-stress concentration region areas, and historical battery SOC, the current rate of change of the high-stress concentration region area with respect to battery SOC is determined. If the current rate of change exceeds a preset threshold, the current battery SOC is substituted into the SOC-negative electrode potential mapping model, achieving timely and accurate assessment of the current battery negative electrode potential. This application establishes a correlation between the high-stress concentration region area and the battery negative electrode potential by linking the rate of change of the high-stress concentration region area with respect to battery SOC to the battery phase transition stage, and by establishing the SOC-negative electrode potential mapping relationship. Based on this correlation, the battery negative electrode potential can be monitored and assessed without relying on complex electrochemical models or additional hardware. This not only has low computational cost and good real-time performance, enabling large-scale deployment in vehicles, but also facilitates early identification of impending lithium plating before it occurs, providing early warning of lithium plating risks.

[0111] To enable those skilled in the art to better understand the embodiments of this application, a specific embodiment is described below.

[0112] During the charging and discharging process of lithium-ion batteries, the electrode materials undergo multiphase transitions (phase transitions). Taking lithium iron phosphate (LFP) batteries as an example, there is a clear two-phase reaction during charging and discharging, exhibiting a "three-stage" characteristic of the voltage plateau; ternary (NCM) batteries, on the other hand, exhibit a more complex multiphase transition, displaying a "four-stage" characteristic. During the phase transition, the lattice constant and volume of the electrode materials change, leading to a reconstruction of the stress distribution on the battery surface. Related studies use overall expansion force or average stress to monitor battery status, but have not focused on the evolution of high-stress concentration regions (i.e., regions where local stress is significantly higher than average stress) and their correlation with phase transitions.

[0113] Existing methods for monitoring negative electrode potential have the following technical problems: (1) Existing negative electrode potential monitoring methods rely on complex electrochemical models or additional hardware, resulting in high computational costs, poor real-time performance, and difficulty in large-scale deployment at the vehicle end. Consequently, the early warning timeliness of lithium plating detection methods based on negative electrode potential monitoring is insufficient, and they can often only be detected after lithium plating has already occurred, resulting in insufficient sensitivity.

[0114] (2) Existing stress sensor applications are limited to measuring the absolute value of expansion force, failing to fully explore the physical correlation between stress distribution characteristics and internal electrochemical state, resulting in low utilization efficiency of stress information. Although related studies have used stress distribution matrices to extract features such as average stress, standard deviation, and coefficient of variation for SOC estimation, they have not yet revealed the physical mapping relationship between the inflection point where stress distribution disorder changes from decreasing to rapidly increasing and the negative electrode potential, let alone used it for early identification of lithium plating risk.

[0115] Extensive experiments revealed that the area of ​​high-stress concentration regions on the battery surface exhibits a step-like evolution curve with changes in State of Charge (SOC) corresponding to the phase transition stages. At the end of charging (the final phase transition stage), the area of ​​high-stress concentration regions increases rapidly, and the SOC at this inflection point has a quantitative mapping relationship with the negative electrode potential. By detecting this inflection point, the negative electrode potential can be indirectly inferred, thus enabling early warning of lithium plating risk.

[0116] The fundamental principle of this application is that the area of ​​the high-stress concentration region on the battery surface (defined as the region composed of all sensing units with stress data > 1.5 × average stress) exhibits a step-like evolution curve corresponding to the phase transition stages as the State of Charge (SOC) changes. Taking lithium iron phosphate batteries as an example, there are three phase transition stages, and the curve showing the change of the high-stress concentration region area with respect to the battery SOC exhibits a "three-segment" change, with the curve showing a rapid upward trend in the last stage (the end of charging). Taking ternary lithium batteries as an example, there are four phase transition stages, and the curve showing the change of the high-stress concentration region area with respect to the battery SOC exhibits a "four-segment" change, with the curve also showing a rapid upward trend in the last stage. The SOC corresponding to the inflection point of the rising curve in the last stage (the point where the curve changes from flat to rapidly rising) has a quantitative mapping relationship with the current negative electrode potential of the battery. By pre-calibrating and establishing a mapping relationship model between the inflection point SOC and the negative electrode potential, the current negative electrode potential of the battery can be estimated by detecting the inflection point online in real time during battery charging. When the current negative electrode potential of the battery approaches the lithium plating critical potential, the corresponding charging strategy of the battery is adjusted.

[0117] In one specific embodiment, the battery negative electrode potential assessment method may include the following steps: Step 1: Arrangement of the thin-film pressure sensor array and acquisition of stress data.

[0118] A sensor array is deployed on at least one surface of the battery. The sensor array may contain M×N sensing units, which are distributed in a grid pattern on the battery surface at predetermined intervals, covering the central and edge regions of the battery. The sensors may be flexible thin-film pressure sensors, which possess high flexibility and good surface adhesion, enabling accurate measurement of stress distribution at different locations on the battery surface. The stress data collected by each sensing unit can be denoted as σ(i,j,t), where (i,j) are the position coordinates of the sensing unit in the sensor array, and t is time.

[0119] During battery charging, stress data of each sensing unit is continuously collected at a preset sampling frequency f, while the current battery SOC and charging current are recorded.

[0120] Step 2: Definition and calculation of high stress concentration areas.

[0121] For each sampling time, calculate the average value μ of the stress data collected by all sensing units. The criteria for determining high stress concentration regions can be defined as follows: σ(i,j)>k×μ; Where μ is the average stress value; σ(i,j) is the stress data collected by the sensing unit; k is a preset coefficient (i.e., a preset multiple), which can be set to k=1.5, meaning that sensing units with stress data greater than 1.5 times the average stress value are considered high stress concentration points.

[0122] The area R_HS of the high stress concentration region can be calculated as follows: R_HS = (Number of sensing units that satisfy σ(i,j)>1.5μ) × Area of ​​a single sensor unit.

[0123] Step 3: Offline calibration – Establish the relationship between the proportional curve of the high stress concentration area and the negative electrode potential.

[0124] A three-electrode battery (including a reference electrode) of the same model was fabricated, and constant current charging experiments were conducted at different charging rates (e.g., 0.05C, 0.1C, 0.33C, 0.5C, 1C, 1.5C) and at different temperatures. Stress data from the thin-film sensor array were collected simultaneously, and the curve of R_HS versus SOC (i.e., the R_HS(SOC) curve) and the negative electrode potential V_neg of the three-electrode battery were calculated.

[0125] Analyze the R_HS(SOC) curve. For lithium iron phosphate batteries, the R_HS(SOC) curve exhibits a typical three-segment characteristic: First stage (SOC around 0%-18%): R_HS decreases rapidly; Second stage (SOC around 18%-80%): R_HS platform; The third stage (SOC around 80%-100%): R_HS rises rapidly.

[0126] The inflection point of the last stage can be defined as the point at which the first derivative of the R_HS(SOC) curve, d(R_HS) / d(SOC), changes from a flat (close to zero) value to a significantly positive value. The battery SOC corresponding to the inflection point can be denoted as SOC_kink.

[0127] Record the SOC_kink and its corresponding negative electrode potential V_neg (obtained through a three-electrode cell) under each operating condition. Experiments show that SOC_kink and V_neg have a good linear or monotonic relationship. Establish a mapping model between SOC and negative electrode potential: V_neg=f(SOC_kink); The mapping relationship model between SOC and negative electrode potential can be stored using linear regression, polynomial fitting, or table lookup.

[0128] Step 4: Online inflection point detection and negative electrode potential estimation.

[0129] During actual on-board charging, the battery negative electrode potential assessment system (specifically, a BMS, Battery Management System) can calculate the R_HS(SOC) curve and its first derivative in real time (the first derivative is the rate of change of the area of ​​the high stress concentration region with respect to the battery SOC). When the current rate of change d(R_HS) / d(SOC) is detected to jump from near zero to greater than a preset rate of change threshold, an inflection point can be determined, and the current SOC_kink is recorded.

[0130] Substitute the current SOC_kink into the mapping relationship model between SOC and negative electrode potential to estimate the current battery negative electrode potential V_neg_est.

[0131] Step 5: Adjust the charging strategy.

[0132] Compare the current battery negative electrode potential V_neg_est with the preset lithium plating critical potential V_threshold (usually 0V, but a custom preset lithium plating critical potential of 20mV can also be defined): If V_neg_est > V_threshold, it is safe: continue or maintain charging; If V_neg_est≤V_threshold, trigger protection: reduce charging current or suspend charging.

[0133] Alternatively, a preset safe SOC threshold can be set directly: the critical SOC_kink_th when V_neg = V_threshold is determined through calibration, and this critical SOC_kink_th is used as the preset safe SOC threshold. When the current battery SOC is detected online to be less than or equal to the preset safe SOC threshold, the current charging strategy corresponding to the battery is adjusted.

[0134] Compared with the prior art, the technical solution provided in this application has the following significant advantages: (1) A “characteristic quantity early warning” for lithium plating risk has been realized. By detecting the rising inflection point of the rate of change of the area of ​​high stress concentration region with SOC, the current negative electrode potential of the battery is calculated based on the SOC corresponding to the rising inflection point, so that the risk of lithium plating can be identified and intervened in time before the current negative electrode potential drops to 0V.

[0135] (2) For the first time, a direct correlation was established between the evolution of high stress concentration regions and the phase transition stages of the battery, and the inflection point of the last phase transition stage was used as a proxy characteristic quantity of the negative electrode potential. Based on the three-stage stress evolution characteristics of lithium iron phosphate batteries and the four-stage stress evolution characteristics of ternary batteries, timely and accurate assessment of the negative electrode potential of the battery was achieved.

[0136] (3) Compared with the method based on the standard deviation of stress distribution, the area of ​​high stress concentration region (or the proportion of high stress concentration region area) is more sensitive to local extreme expansion and has a higher signal-to-noise ratio. The standard deviation of stress distribution can only reflect the overall dispersion, while the high stress concentration region directly captures the areas that need to be focused on. These areas are often the locations where lithium plating is most likely to occur first. Therefore, the early warning of lithium plating risk is more direct and reliable.

[0137] (4) It is universal for different battery systems (lithium iron phosphate batteries, ternary batteries). Only the inflection point detection logic needs to be adjusted according to the number of phase transition segments (detecting the last rising segment), and after calibration, it can be applied to a variety of battery systems.

[0138] (5) It has the ability to adapt to charging rate and temperature. The SOC corresponding to the inflection point is different at different rates, but the mapping relationship model between SOC and negative electrode potential can be used in a wide range of operating conditions after calibration.

[0139] Below is a specific example.

[0140] At an ambient temperature of 25°C, a three-electrode battery was used for constant current charging at rates of 0.05C, 0.1C, 0.33C, 0.5C, 1.0C, 1.2C, 1.5C, and 2.0C, respectively. Simultaneously, stress data from the thin-film sensor (for calculating R_HS, k=1.5) and the negative electrode potential were acquired.

[0141] For each rate, the inflection point of the last rising segment of the R_HS(SOC) curve was determined and recorded as the inflection point SOC_kink and the corresponding negative electrode potential V_neg. The results are shown in Table 1: Table 1

[0142] V_neg=0.0121×SOC_kink-0.869, correlation coefficient R 2 =0.994.

[0143] Wherein, V_neg(V vs Li / Li) + ) indicates Li / Li + The negative electrode potential value measured or calculated by using the redox couple as a reference electrode.

[0144] This result demonstrates a highly stable linear mapping relationship between the state of charge (SOC) at the inflection point and the negative electrode potential. The lithium plating critical potential was set to 0V (vs Li / Li). + Substituting this into the mapping model between SOC and negative electrode potential, we obtain the corresponding critical inflection point SOC_kink_th≈ 71.8%. That is, when the current battery SOC is detected to be ≤71.8% online, it indicates that the current negative electrode potential may be less than 0V, and the risk of lithium plating is high.

[0145] The calibrated mapping model between SOC and negative electrode potential is stored in the BMS. The same lithium iron phosphate battery (without three electrodes) is installed in a vehicle and charged at a 1.2C rate at 25°C. The R_HS(SOC) curve and its first derivative are calculated in real time.

[0146] When the battery is charged to 72.3% SOC, the BMS detects that d(R_HS) / d(SOC) (i.e., the rate of change) jumps from 0.1% / SOC to 2.2% / SOC, indicating an inflection point has occurred, and records the current SOC_kink = 72.33%. Substituting the current battery SOC into the mapping relationship model between SOC and negative electrode potential, the current negative electrode potential V_neg_est = 0.0121 × 72.33 - 0.869 ≈ -0.0053V is obtained, which is below 0V. At this point, the BMS immediately triggers a protective charging strategy.

[0147] Figure 6 This is a schematic diagram of the structure of an electric vehicle provided in an embodiment of this application. (Refer to...) Figure 6 This application provides an electric vehicle, including a battery pack 610 and a battery negative electrode potential assessment system 620 described above. The battery pack 610 includes multiple batteries. For each battery, in the on-board charging state, the battery negative electrode potential assessment system 620 can be used to assess the negative electrode potential of the battery in the battery pack 610 to obtain the current battery negative electrode potential, so as to adjust the current charging strategy corresponding to the battery according to the current battery negative electrode potential.

[0148] Figure 7 This is a schematic diagram of the battery negative electrode potential evaluation device provided in an embodiment of this application. (Refer to...) Figure 7 This application provides a battery negative electrode potential evaluation device, which may specifically include the following modules: The high-stress area determination module 710 is used to determine the current high-stress concentration area and the area of ​​the current high-stress concentration area based on the current battery surface stress data. The rate of change determination module 720 is used to determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC. The negative electrode potential determination module 730 is used to substitute the current battery SOC into the mapping relationship model between SOC and negative electrode potential when the current rate of change is greater than a preset rate of change threshold, so as to obtain the current battery negative electrode potential.

[0149] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute the following methods: Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] On the other hand, this application discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer can execute the methods provided in the above-described method embodiments, such as: determining a current high-stress concentration region based on current battery surface stress data, and determining the area of ​​the current high-stress concentration region; determining the current rate of change of the high-stress concentration region area with respect to the battery SOC based on the current high-stress concentration region area, the current battery SOC, the historical high-stress concentration region area, and the historical battery SOC; and, if the current rate of change is greater than a preset rate of change threshold, substituting the current battery SOC into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0151] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the methods provided in the above embodiments, such as: determining a current high-stress concentration region based on current battery surface stress data, and determining the area of ​​the current high-stress concentration region; determining the current rate of change of the area of ​​the high-stress concentration region with respect to the battery SOC based on the current area of ​​the high-stress concentration region, the current battery SOC, the historical area of ​​the high-stress concentration region, and the historical battery SOC; and, if the current rate of change is greater than a preset rate of change threshold, substituting the current battery SOC into a mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0153] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate this application and are not intended to limit this application. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that various combinations, modifications, or equivalent substitutions of the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application and should be covered within the scope of the claims of this application.

Claims

1. A battery negative electrode potential evaluation system, characterized in that, include: Data acquisition equipment is used to collect battery surface stress data; The controller is configured to: Based on the current battery surface stress data, determine the current high stress concentration area and its area; Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC. If the current rate of change is greater than a preset rate of change threshold, the current battery SOC is substituted into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

2. The battery negative electrode potential evaluation system according to claim 1, characterized in that, The data acquisition device is a sensor array deployed on the surface of the battery. The sensor array includes multiple sensing units, and the battery surface stress data is the stress data of each sensing unit. The process of determining the current high-stress concentration area based on the current battery surface stress data includes: Based on the current stress data of each sensing unit, the sensing unit whose current stress data meets the criteria for high stress concentration point determination is identified as the current high stress concentration point. Based on the location of each current high stress concentration point, the current high stress concentration area is determined.

3. The battery negative electrode potential evaluation system according to claim 2, characterized in that, The condition for determining the high stress concentration point is that the current stress data is greater than the current average stress value by a preset multiple; wherein, the preset multiple is greater than 1, and the current average stress value is calculated based on the current stress data of each sensing unit.

4. The battery negative electrode potential evaluation system according to claim 1, characterized in that, The data acquisition device is a sensor array deployed on the surface of the battery, and the sensor array includes multiple sensing units; The current high-stress concentration area includes multiple current high-stress concentration points; Determining the area of ​​the current high-stress concentration region includes: Calculate the area of ​​the current high stress concentration region based on the number of current high stress concentration points and the area of ​​a single sensing unit.

5. The battery negative electrode potential evaluation system according to claim 1, characterized in that, The determination of the current rate of change of the area of ​​the high-stress concentration region with respect to the battery SOC based on the current area of ​​the high-stress concentration region, the current battery SOC, the historical area of ​​the high-stress concentration region, and the historical battery SOC includes: Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, a curve showing the current relationship between the area of ​​the high stress concentration region and the battery SOC is fitted. Calculate the derivative of the current relationship curve at the current battery SOC to obtain the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC.

6. The battery negative electrode potential evaluation system according to claim 1, characterized in that, The controller is also configured to: Based on the comparison between the current negative electrode potential of the battery and the preset safety potential threshold, the current charging strategy corresponding to the battery is determined.

7. The battery negative electrode potential evaluation system according to claim 6, characterized in that, The step of determining the current charging strategy for the battery based on the comparison result between the current battery negative electrode potential and the preset safety potential threshold includes: If the current negative electrode potential of the battery is greater than the preset lithium plating critical potential, the current charging strategy corresponding to the battery is determined to be the continue charging strategy. If the current negative electrode potential of the battery is less than or equal to the preset lithium plating critical potential, the current charging strategy corresponding to the battery is determined to be a protective charging strategy.

8. The battery negative electrode potential evaluation system according to any one of claims 1-7, characterized in that, The mapping model between SOC and negative electrode potential was obtained by calibration in the following way: Based on the continuously collected battery surface stress data and battery SOC under different operating conditions, the variation curves of the area of ​​high stress concentration region with battery SOC under different operating conditions were determined. For each change curve, the battery SOC corresponding to the point where the rate of change of the change curve is greater than a preset positive value is determined as the battery SOC calibration value. Based on the battery SOC calibration value and the corresponding battery negative electrode potential under different operating conditions, a mapping relationship model between SOC and negative electrode potential is established.

9. The battery negative electrode potential evaluation system according to claim 8, characterized in that, The battery is a lithium iron phosphate battery or a ternary battery; When the battery is a lithium iron phosphate battery, the curve showing the change of the area of ​​the high stress concentration region with respect to the battery SOC presents a three-stage change corresponding to the phase transition stage, and the curve shows an upward trend in the last stage of the three stages. When the battery is a ternary lithium battery, the curve showing the change of the area of ​​the high stress concentration region with respect to the battery's SOC exhibits a four-segment change corresponding to the phase transition stage, with the curve showing an upward trend in the last stage of the four segments.

10. The battery negative electrode potential evaluation system according to claim 8, characterized in that, The mapping relationship model between SOC and negative electrode potential is stored using any one of linear regression, polynomial fitting, or table lookup method.

11. The battery negative electrode potential evaluation system according to claim 1, characterized in that, If the current rate of change is greater than a preset rate of change threshold, the controller is further configured to: If the current battery SOC is less than or equal to a preset safe SOC threshold, the current charging strategy corresponding to the battery is adjusted.

12. A method for evaluating the negative electrode potential of a battery, characterized in that, include: Based on the current battery surface stress data, determine the current high stress concentration area and its area; Based on the current area of ​​the high stress concentration region, the current battery SOC, the historical area of ​​the high stress concentration region, and the historical battery SOC, determine the current rate of change of the area of ​​the high stress concentration region with respect to the battery SOC. If the current rate of change is greater than a preset rate of change threshold, the current battery SOC is substituted into the mapping relationship model between SOC and negative electrode potential to obtain the current battery negative electrode potential.

13. An electric vehicle, characterized in that, The invention includes a battery pack and a battery negative electrode potential assessment system according to any one of claims 1-11, wherein the battery pack comprises a plurality of batteries; For each battery, under on-board charging conditions, the negative electrode potential of the battery is evaluated using the battery negative electrode potential evaluation system to obtain the current negative electrode potential of the battery, so as to adjust the current charging strategy corresponding to the battery according to the current negative electrode potential of the battery.

Images